Alexander Immer, MSc
"You can’t connect the dots looking forward; you can only connect them looking backwards." - Steve Jobs
I am interested in probabilistic inference for flexible models like neural networks and how it can help improving biomedical applications.
I received my BSc in IT-Systems Engineering from Hasso Plattner Institute in Potsdam where I first got in contact with data science. During my MSc studies at EPFL, I became interested in approximate Bayesian inference which I further pursued during my time at RIKEN AIP in Tokyo. Since July 2020, I am a PhD student within the Max-Planck ETH Center for Learning Systems where I am supervised by Gunnar Rätsch and Bernhard Schölkopf. My goal is to design machine learning algorithms that can incorporate prior knowledge, quantify uncertainty, and automatically select the most likely model given data. Apart from that, these algorithms need to be practical and interpretable to be relevant to biomedical applications.
Please consult my website for details on current and previous projects.